All scripts can be executed using Node.js. Replace
and
with actual values.
Note: The scripts automatically load the environment variables from various .env files. Do not ask the user to set vars unless skill executions fails due to env var absence.
Use this skill to analyze the contribution about changes to key metrics in multi-dimensional data.
Provides the expression to use to calculate the metric you are analyzing.
To calculate a summable metric, the expression must be in the form SUM(metric_column_name),
where metric_column_name is a numeric data type.
To calculate a summable ratio metric, the expression must be in the form
SUM(numerator_metric_column_name)/SUM(denominator_metric_column_name),
where numerator_metric_column_name and denominator_metric_column_name are numeric data types.
To calculate a summable by category metric, the expression must be in the form
SUM(metric_sum_column_name)/COUNT(DISTINCT categorical_column_name). The summed column must be a numeric data type.
The categorical column must have type BOOL, DATE, DATETIME, TIME, TIMESTAMP, STRING, or INT64. | Yes | |
| is_test_col | string | The name of the column that identifies whether a row is in the test or control group. | Yes | |
| dimension_id_cols | array | An array of column names that uniquely identify each dimension. | No | |
| top_k_insights_by_apriori_support | integer | The number of top insights to return, ranked by apriori support. | No |
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| pruning_method | string | The method to use for pruning redundant insights. Can be 'NO_PRUNING' or 'PRUNE_REDUNDANT_INSIGHTS'. | No |
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Use this skill to perform data analysis, get insights,
or answer complex questions about the contents of specific
BigQuery tables.
Use this skill to forecast time series data.
Use this skill to find tables, views, models, routines or connections.